Machine learning helps us solve various modern-day problems, and we’re talking not only about data scientists or developers but about typical app users. Modern tools for machine learning are limited only by one’s imagination, and the availability of resources needed to make ideas come to life. And while the first part of this equation entirely depends on developers’ expertise, the second is all about companies that are interested in enhancing machine learning technologies.  

Machine learning is already progressed to the point where companies can sell it as a service. If there are PaaS and SaaS solutions, so why not market machine learning the same way, right? MLaaS usually combines automated and partially automated cloud platforms that take care of pre-processing routines, model training, and evaluation. Plus, they can also make predictions. With the help of RESTful APIs, you can link those predictions with your internal IT infrastructure. We already covered machine learning services offered by Amazon and Microsoft, so today, we’re going to be looking at the ML products engineered by another IT giant from the grand trio – Google. 

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In numerous presentations throughout the last decade, Google has proclaimed that its arsenal of ML tools and solutions is one of their main competitive advantages in the war for the machine learning market. Also, it has been said that their main goal is to deliver the fastest and easiest machine learning experience for developers of all skill levels – from entry to senior practitioners. Let’s dive into Google’s world and discover what they have already achieved in the field of machine learning.  

What Is Google’s Angle on Machine Learning? 

We already pointed out one aspect that fuels Google's interest in ML – the competition in the IT market. But there’s more to it, of course. Machine learning today progresses with each passing year, opening new horizons for technological advancement. Bright minds at Google understand that all services must be updated and improved to make them relevant for the audience. 

The abundance of information makes it practically impossible for humans to know and remember everything, so making machines for performing various data-heavy tasks (e.g., forecasting) sounds like a great idea. And it is. Even though the Internet is a limitless source of information for machine learning, without proper tools, machines can’t be taught. Thankfully, the last decade was fruitful for incredibly complex and powerful machine learning programs created by many IT companies, including Google. 

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But unlike many others, lesser service providers, Google realizes that all existing instruments are not ideal and require numerous improvements. This means that tremendous processing power and dedicated resources for parallel operations are required. Smaller companies don’t have enough human and financial resources to venture into deep machine learning, but Google, on the other hand, does. Utilizing the latest advances in the machine learning sphere, this company never ceases to experiment and extend the range of their ML services, while making them affordable for the mass market.  

What ML Products Google Has to Offer? 

Google loves to call itself an AI-first company, which implies that they strive to use artificial intelligence in as many products and services as possible. And it is safe to say that they are doing it pretty well. The original idea of ML integration within Google dates back to the active stages of Gmail enhancement and infamous spam wars. But today, machine learning solutions are much more diverse. 

For instance, when you are looking for something on the Web using Google search, the search results are ranked by AI. Also, machine learning is integrated into Google Assistant, various Android handwriting recognition apps, and services like Google Images and Google Translate. Moreover, not many YouTube and Google Play users realize that the recommendations they see are also made possible thanks to machine learning.

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Google utilizes ML capabilities wherever it is possible. Their signature keyboard Gboard gets improvements for manual and voice commands based on machine learning algorithms. Even simple tasks like text selection on Android devices is done with the help of a neural network that automatically marks addresses and phone numbers.

What we really love about machine learning is that it provides unique features for software products. A perfect example would be the Smart Reply function that was added in Gmail a few years ago. The engineers thought what if they could use machine learning for automatic generation of replies to save Gmail users from manual typing. If you are using Gmail Inbox, you can check this feature by yourself, and chances are, you are going to like it. Sending a quick reply is very simple: select one of three available response options and click Send. According to the official Google statistics, each 10th Gmail auto-reply is generated with machine learning algorithms. 

Google Machine Learning Engine

The machine learning engine is really a great way to do distributed learning. It is a managed service, so you connect your model and your data, and then just spin it up. The engine takes care of all the distributed learning for you. All unnecessary things are stripped down in the process, so you will be paying only for what you actually use. 

The other nice thing about Google ML Engine is that it does batch prediction and online prediction. Give it a test run, and you will see that batch prediction jobs can be done easier than ever before. As for an online prediction, it will create a REST API for you and any model of your choice and serve it to whatever volumes you need, for any number of apps you have. As you see, it is a great tool to manage your applications straightforwardly.

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Google Cloud ML Engine is all about flexibility, which makes it the “go-to” choice for many developers. It is oriented at cloud infrastructure backed up by TensorFlow as the main ML driver, which reminds about another similar solution – SageMaker by AWS.    

TensorFlow is one of the most popular open-source libraries packed with a multitude of data science tools for machine learning projects, and it also belongs to Google. Its only minus is that it lacks a GUI, which makes the learning process quite tedious for beginners. But software engineers that already have some background in data science shouldn’t experience any major difficulties with TensorFlow. 

ML APIs 

When you’re not ready to create your own ML models, you can still work with a wide range of APIs that come packed with pre-trained models. All you have to do is to prepare data and let them learn. So even if you have zero practical experience with machine learning, Google APIs can save the day. Currently, the company offers tools for textual analysis, text/image/video recognition, translation (100+ languages), and some other related services. 

It is obvious that Amazon and Microsoft also have similar services, but Google offers a number of truly unique and more advanced solutions. There are three main categories of ML APIs:

  • Sight: object and face detection, video search, optical character recognition.
  • Conversation: audio-to-text, voice synthesis, chatbots.
  • Language: meaning-extraction and building structure, sentiment analysis, translation.  

Let’s quickly look at each one of these categories in order to explain core out-of-the-box capabilities that you can use without any coding other than REST API and then implement the final result into your apps. 

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Sight category

Object and face detection: You have an image, and you want to figure out what’s on it automatically. All you need to do is to pass that image to the Vision API, and it will tell you what are the objects on that picture. It will be to determine where the faces are and distinguish different people as well. 

Video search: you can pass any video for the video analysis API, and it will mark scene transitions, detect different objects, and so on. This way, you can find whatever you want within a video (or cross video if you are working with multiple clips). 

Optical character recognition (OCR): people often work with document scans (PDF, JPEG, or other similar formats), and sometimes they need to understand clearly what is contained in those documents. After you pass a scan to the Google API, it will automatically detect the words in that file and give you a raw text afterwards. The API can also detect things like signs on the streets and give you the text written on them. This API does a fantastic job for a wide range of everyday applications. 

Conversation category

Audio-to-text: comes in handy when you need to know what is said in audio files. The API can transcribe everything and give you text. This technology is often used for videos (automatic subtitles) and interviews with real people on TV shows. 

Voice synthesis: it is a reverse of the previous feature. The API will read your text with a synthetic voice.

Chatbots: provides means for interacting with users, builds a platform for conversations you would like to have with your audience. The most common example of using chatbot APIs is to provide customer support on e-commerce websites.

Language category

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Meaning extraction + structuring: helps with defining the meaning of free form texts. The API can figure out and map out the context, build the data structure of the text, so you will be able to tell what parts of speech are used and in what relationships.

Sentiment analysis: helps you to determine the mood of a text. Depending on your needs, the API will categorize all texts you are receiving (e.g., email, chats) and labelling them as aggressive, calm, happy, and so on. This way, you will always be sure that things won’t spiral out of control.

Translation: a great way to translate from one language to another.

ML APIs are simply REST calls, which can be made from any language or framework. Now let’s talk about some products offered by Google. Our shortlist of notable APIs includes Dialogflow, Natural Language, Cloud Speech-to-Text, Translation API, Vision AI, and Video AI.

Dialogflow

Chatbots have become a popular solution for helping various businesses and guiding their clients. And Google has a dedicated solution for this. Dialogflow excels at text analysis, determining the intents of clients using NLP technologies. It is extra-flexible and can be customized for your needs using Python, Java, and Node.js. 

Natural Language

This tool provides insights from any unstructured text and works similar to Amazon’s Comprehend and Microsoft’s Language APIs. Using pre-trained models, you can work with natural language, which simplifies the process of syntax and sentiment analysis as well as content classification. Natural Language is an excellent choice for:

  • Recognizing sentiments in text.
  • Defining entities.
  • Categorizing items (e.g., cybersecurity, updates, releases, etc.).
  • Analyzing syntax structures.

Cloud Speech-to-Text

The main benefit of this natural speech recognition service is the sheer number of supported languages. More than 100 languages (including some variants and dialects) can be transformed from the audio form into text. This tool also comes packed with some extra features: 

  • Word hints – advanced tunable speech recognition for specific contexts and narrow niches. Performs equally good for transcribing industry-related topics and area-specific word forms. 
  • Automatic filtering of the content that contains slurs, curses, and other inappropriate things.
  • Automatic detection of the spoken language.
  • Perfectly handles noisy audio without any additional noise cancellation tools.

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Cloud Translation

The one API you will definitely want to use for implementing Google translation services in your apps and other products. It provides a dynamic translation for 120 languages with the ability to recognize the language used automatically. Fast operation and dynamic translation are guaranteed by pre-trained models; however, nothing stops you from working with custom-made ML models. Consists of AutoML Translation and two Translation APIs – Basic and Advanced.

  • AutoML Translation: whether you are a linguist, localization manager, or entry-level ML developer, this service lets you create high-grade ML models for production with ease. 
  • Basic Translation API: instant text translation from and into any of the available languages. Utilizes neural machine translation (pre-trained models).
  • Advanced Translation API: an upgraded version of the previous API. It has a few great features to support customization, which is crucial for defining the meaning of terms and complex phrases. 

Vision API

One of the best tools for image recognition by Google. Capable of assigning labels to images for further classification into predefined categories. Vision API can detect faces, objects, and text (both handwritten and printed). Other notable features are the following:

  • Recognition of facial expressions.
  • Detecting landmarks and adding tags (vacation, birthday, etc.).
  • Language identification.
  • Dominant colour identification.

Video Intelligence API

This API works both with streaming and pre-recorded video and utilizes pre-trained ML models to detect actions, objects, and places filmed. Google is actively working on this API by adding new concepts to the feature list. Besides the functions mentioned above, VI API can also:

  • Do object labelling.
  • Identify inappropriate content.
  • Transcribe speech. 

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Cloud AutoML 

The technology that automatically creates customized machine learning models. AutoML provides various, trained to a certain degree, machine learning models created with the Neural Architecture Search algorithm. What makes this algorithm genuinely unique is that it not just processes images but also tries to find an optimal neural network for each particular task and automatically uses it.  

With AutoML, you get access to a variety of tools designed to train neural networks to the desired level incredibly fast. These tools enable you to use a full spectre of AI capabilities for solving every day business-related tasks such as:  

  • Automatic data gathering and processing.
  • Identification of key parameters, meta-training, and transfer learning.
  • Automated model selection.
  • Automatic checking for possible errors.
  • Leak detection.
  • Automatic analysis of errors.
  • Automated validation procedures.
  • Metrics evaluation.

It is very easy to use this service, and you don’t need extensive ML expertise to take advantage of its features.

Conclusion

Google truly deserves a spot on the pedestal of the greatest digital innovators, and their range of ML services is yet another proof to that statement. Even though all major corporations are trying to make their machine learning tools more accessible and user-friendly, you still need ML practitioners or data scientists on your side to make sure everything goes according to your plan. Here, at SSA Group, we’re always ready to accept new challenges and help our clients with adapting the power of machine learning for their businesses. Our specialists will help you decide which provider of ML services to choose and how to make the most out of it - contact us today!